{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qlseAUvJJDXM"
   },
   "source": [
    "## The Data\n",
    "\n",
    "** Source: https://datamarket.com/data/set/22ox/monthly-milk-production-pounds-per-cow-jan-62-dec-75#!ds=22ox&display=line **\n",
    "\n",
    "**Monthly milk production: pounds per cow. Jan 62 - Dec 75**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5WTUlbh4JDXN"
   },
   "source": [
    "** Import numpy pandas and matplotlib **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xg2peEzGJDXO"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hBtEtgN6JDXS"
   },
   "source": [
    "** Use pandas to read the csv of the monthly-milk-production.csv file and set index_col='Month' **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 29701,
     "status": "ok",
     "timestamp": 1555021285621,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "FPJdTW0TJcMA",
    "outputId": "8931b8b6-ed4f-4b22-83ba-e056d7225711"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive \n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nArYXHMPJDXT"
   },
   "outputs": [],
   "source": [
    "milk = pd.read_csv(\"gdrive/My Drive/dataML/monthly-milk-production.csv\",index_col='Month')\n",
    "\n",
    "#milk = pd.read_csv(\"monthly-milk-production.csv\",index_col='Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 30254,
     "status": "ok",
     "timestamp": 1555021286232,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "RYvb00fEJDXW",
    "outputId": "e9a410e1-9495-49fb-f98d-44a503412335"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Milk Production</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-01 01:00:00</th>\n",
       "      <td>589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-02-01 01:00:00</th>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03-01 01:00:00</th>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04-01 01:00:00</th>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05-01 01:00:00</th>\n",
       "      <td>727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Milk Production\n",
       "Month                               \n",
       "1962-01-01 01:00:00            589.0\n",
       "1962-02-01 01:00:00            561.0\n",
       "1962-03-01 01:00:00            640.0\n",
       "1962-04-01 01:00:00            656.0\n",
       "1962-05-01 01:00:00            727.0"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qd6qU_zOJDXb"
   },
   "source": [
    "** Check out the head of the dataframe**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z0aPxAkWJDXg"
   },
   "source": [
    "** Make the index a time series by using: **\n",
    "\n",
    "    milk.index = pd.to_datetime(milk.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ej2PnSz7JDXh"
   },
   "outputs": [],
   "source": [
    "milk.index = pd.to_datetime(milk.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 30214,
     "status": "ok",
     "timestamp": 1555021286237,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "TuNcJhMoJDXj",
    "outputId": "60ba7680-626f-4005-a054-3e8f00373a1b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Milk Production</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-01 01:00:00</th>\n",
       "      <td>589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-02-01 01:00:00</th>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03-01 01:00:00</th>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04-01 01:00:00</th>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05-01 01:00:00</th>\n",
       "      <td>727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Milk Production\n",
       "Month                               \n",
       "1962-01-01 01:00:00            589.0\n",
       "1962-02-01 01:00:00            561.0\n",
       "1962-03-01 01:00:00            640.0\n",
       "1962-04-01 01:00:00            656.0\n",
       "1962-05-01 01:00:00            727.0"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 30197,
     "status": "ok",
     "timestamp": 1555021286239,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "-rgkKTs6JDXn",
    "outputId": "d0c2ed31-4719-4853-d701-07057a196c50"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Milk Production'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z8Hk38WeJDXq"
   },
   "source": [
    "** Plot out the time series data. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 30575,
     "status": "ok",
     "timestamp": 1555021286647,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "qXNiSsZUJDXq",
    "outputId": "5ccde4fd-6ca2-4814-8c3b-5aea28a11776"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fde3a883240>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PoVyh+MAVtcPLmYXV6cnULgxtxlJKv0spvZxSei2AFQBHAcwzS0b5uqA8fRpyxM8YU+6r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\ncZwG5131vrncbqgAeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "milk.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Fxbb9fhP1rsU"
   },
   "source": [
    "### Train Test Split\n",
    "\n",
    "** Let's attempt to predict a year's worth of data. (12 months or 12 steps into the future) **\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 30554,
     "status": "ok",
     "timestamp": 1555021286649,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "TSYpC-nu1rsW",
    "outputId": "90d4c5be-de98-4817-c3af-0281811c8963"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 168 entries, 1962-01-01 01:00:00 to 1975-12-01 01:00:00\n",
      "Data columns (total 1 columns):\n",
      "Milk Production    168 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 2.6 KB\n"
     ]
    }
   ],
   "source": [
    "milk.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 30545,
     "status": "ok",
     "timestamp": 1555021286657,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "00vc2Jms1rsc",
    "outputId": "0b5d35bc-954b-438f-a98e-360412250e15"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "168"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(milk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F_PjB7rq1rsf"
   },
   "outputs": [],
   "source": [
    "train_set= milk.head((1976-1962-1)*12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C6632CCp1rsi"
   },
   "outputs": [],
   "source": [
    "test_set = milk.tail(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_9PtX6Um1rsl"
   },
   "source": [
    "### Scale the Data\n",
    "\n",
    "** Use sklearn.preprocessing to scale the data using the MinMaxScaler. Remember to only fit_transform on the training data, then transform the test data. You shouldn't fit on the test data as well, otherwise you are assuming you would know about future behavior!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "czuAVyRj1rsl"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UFn7xXzj1rsp"
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hu0-OYDX1rsq"
   },
   "outputs": [],
   "source": [
    "train_scaled = scaler.fit_transform(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O2uDgWsI1rsu"
   },
   "outputs": [],
   "source": [
    "test_scaled = scaler.transform(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EiI-RuSPKC7v"
   },
   "source": [
    "## Build Training Data\n",
    "\n",
    "* X_train: Past 12 monthes productions, shape: (-1,12)\n",
    "* Y_train: Future 1 month productions, shape:(-1, 1) \n",
    "* Shift the window to get more trainning data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6YaF3wjKKB4Y"
   },
   "outputs": [],
   "source": [
    "def build_train_data(data, past_monthes = 12, future_monthes = 1):\n",
    "  X_train, Y_train = [],[]\n",
    "  \n",
    "  for i in range(data.shape[0] + 1 - past_monthes - future_monthes):\n",
    "    X_train.append(np.array(data[i:i + past_monthes]))\n",
    "    Y_train.append(np.array(data[i + past_monthes:i + past_monthes + future_monthes]))\n",
    "    \n",
    "  return np.array(X_train).reshape([-1,12]), np.array(Y_train).reshape([-1,1])\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t_z8xNdaQjvH"
   },
   "outputs": [],
   "source": [
    "x, y = build_train_data(train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 31204,
     "status": "ok",
     "timestamp": 1555021287363,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "DiLmBOy4Qq3j",
    "outputId": "1fac8bcf-951c-4790-dfcd-6481685b6d53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x shape; (143, 12) y shape:  (143, 1)\n"
     ]
    }
   ],
   "source": [
    "print('x shape;', x.shape, 'y shape: ', y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JxbPdOLds9JD"
   },
   "source": [
    "Take a look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 31456,
     "status": "ok",
     "timestamp": 1555021287674,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "3_tRprRSjkYY",
    "outputId": "e7bc2d58-9de3-4b90-dcef-9ee2f9badbe6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fde2a622be0>]"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T2WsJDXTwzLQ0/B2e+ajTox+oisgdIpIpIplOp9Mlz3nvxb04L7E9v124id0H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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x[12])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "P_rsY5NQQ3op"
   },
   "source": [
    "## Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1GiFVMqlQ3or"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers, Sequential,models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AsBnLuapQ3ot"
   },
   "outputs": [],
   "source": [
    "RNN_CELLSIZE = 10\n",
    "SEQLEN = 12\n",
    "BATCHSIZE = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j96G7Ef0k5Z-"
   },
   "source": [
    "### Build and Train the Model\n",
    "Many to One"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 343
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 32876,
     "status": "ok",
     "timestamp": 1555021289111,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "vwLHV-p5k5Z0",
    "outputId": "cc239d60-03a5-4b78-ddf5-226aac2506d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "reshape (Reshape)            (None, 12, 1)             0         \n",
      "_________________________________________________________________\n",
      "gru (GRU)                    (None, 12, 10)            360       \n",
      "_________________________________________________________________\n",
      "gru_1 (GRU)                  (None, 10)                630       \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 1,001\n",
      "Trainable params: 1,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_layers = [\n",
    "    layers.Reshape((SEQLEN,1),input_shape=(SEQLEN,)),\n",
    "    layers.GRU(RNN_CELLSIZE, return_sequences=True),\n",
    "    layers.GRU(RNN_CELLSIZE),\n",
    "    layers.Dense(1)\n",
    "    \n",
    "]\n",
    "model = Sequential(model_layers)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 32870,
     "status": "ok",
     "timestamp": 1555021289112,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "qU4zPwkzk5Zt",
    "outputId": "1e5a1f6e-021f-4e72-fecd-6710e476f24d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/losses_utils.py:170: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    }
   ],
   "source": [
    "model.compile(\n",
    "       loss = 'mean_squared_error',\n",
    "       optimizer = 'adam'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34088
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 832811,
     "status": "ok",
     "timestamp": 1555022089082,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "6AW4Vl5-k5ZP",
    "outputId": "2cadc61b-dc26-4113-db05-8a1cf4bb7519"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/1000\n",
      "143/143 [==============================] - 4s 30ms/sample - loss: 0.1687\n",
      "Epoch 2/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0414\n",
      "Epoch 3/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0293\n",
      "Epoch 4/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0263\n",
      "Epoch 5/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0248\n",
      "Epoch 6/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0241\n",
      "Epoch 7/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0233\n",
      "Epoch 8/1000\n",
      "143/143 [==============================] - 1s 7ms/sample - loss: 0.0228\n",
      "Epoch 9/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0222\n",
      "Epoch 10/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0214\n",
      "Epoch 11/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0211\n",
      "Epoch 12/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0203\n",
      "Epoch 13/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0198\n",
      "Epoch 14/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0189\n",
      "Epoch 15/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0184\n",
      "Epoch 16/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0179\n",
      "Epoch 17/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0173\n",
      "Epoch 18/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0169\n",
      "Epoch 19/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0165\n",
      "Epoch 20/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0159\n",
      "Epoch 21/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0156\n",
      "Epoch 22/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0152\n",
      "Epoch 23/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0146\n",
      "Epoch 24/1000\n",
      "143/143 [==============================] - 1s 7ms/sample - loss: 0.0142\n",
      "Epoch 25/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0135\n",
      "Epoch 26/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0130\n",
      "Epoch 27/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0126\n",
      "Epoch 28/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0122\n",
      "Epoch 29/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0117\n",
      "Epoch 30/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0114\n",
      "Epoch 31/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0114\n",
      "Epoch 32/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0115\n",
      "Epoch 33/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0106\n",
      "Epoch 34/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0101\n",
      "Epoch 35/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0103\n",
      "Epoch 36/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0099\n",
      "Epoch 37/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0094\n",
      "Epoch 38/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0091\n",
      "Epoch 39/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0090\n",
      "Epoch 40/1000\n",
      "143/143 [==============================] - 1s 7ms/sample - loss: 0.0088\n",
      "Epoch 41/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0085\n",
      "Epoch 42/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0085\n",
      "Epoch 43/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0084\n",
      "Epoch 44/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0081\n",
      "Epoch 45/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0079\n",
      "Epoch 46/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0078\n",
      "Epoch 47/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0078\n",
      "Epoch 48/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0078\n",
      "Epoch 49/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0081\n",
      "Epoch 50/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0074\n",
      "Epoch 51/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0077\n",
      "Epoch 52/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0071\n",
      "Epoch 53/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0070\n",
      "Epoch 54/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0070\n",
      "Epoch 55/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0069\n",
      "Epoch 56/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0065\n",
      "Epoch 57/1000\n",
      "143/143 [==============================] - 1s 7ms/sample - loss: 0.0065\n",
      "Epoch 58/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0062\n",
      "Epoch 59/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0061\n",
      "Epoch 60/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0064\n",
      "Epoch 61/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0060\n",
      "Epoch 62/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0058\n",
      "Epoch 63/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0057\n",
      "Epoch 64/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0055\n",
      "Epoch 65/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0053\n",
      "Epoch 66/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0058\n",
      "Epoch 67/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0050\n",
      "Epoch 68/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0052\n",
      "Epoch 69/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0049\n",
      "Epoch 70/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0049\n",
      "Epoch 71/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0046\n",
      "Epoch 72/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0049\n",
      "Epoch 73/1000\n",
      "143/143 [==============================] - 1s 7ms/sample - loss: 0.0048\n",
      "Epoch 74/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0046\n",
      "Epoch 75/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0045\n",
      "Epoch 76/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0048\n",
      "Epoch 77/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0047\n",
      "Epoch 78/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0048\n",
      "Epoch 79/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0051\n",
      "Epoch 80/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0044\n",
      "Epoch 81/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0045\n",
      "Epoch 82/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0044\n",
      "Epoch 83/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0047\n",
      "Epoch 84/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0045\n",
      "Epoch 85/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0047\n",
      "Epoch 86/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0044\n",
      "Epoch 87/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0044\n",
      "Epoch 88/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0049\n",
      "Epoch 89/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0049\n",
      "Epoch 90/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0047\n",
      "Epoch 91/1000\n",
      "143/143 [==============================] - 1s 8ms/sample - loss: 0.0045\n",
      "Epoch 92/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0041\n",
      "Epoch 93/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0043\n",
      "Epoch 94/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0048\n",
      "Epoch 95/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0044\n",
      "Epoch 96/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0042\n",
      "Epoch 97/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0040\n",
      "Epoch 98/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0040\n",
      "Epoch 99/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0041\n",
      "Epoch 100/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0040\n",
      "Epoch 101/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0043\n",
      "Epoch 102/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0040\n",
      "Epoch 103/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0041\n",
      "Epoch 104/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0043\n",
      "Epoch 105/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0049\n",
      "Epoch 106/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0046\n",
      "Epoch 107/1000\n",
      "143/143 [==============================] - 1s 7ms/sample - loss: 0.0043\n",
      "Epoch 108/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0039\n",
      "Epoch 109/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0039\n",
      "Epoch 110/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0038\n",
      "Epoch 111/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0039\n",
      "Epoch 112/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0038\n",
      "Epoch 113/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0039\n",
      "Epoch 114/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0037\n",
      "Epoch 115/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0037\n",
      "Epoch 116/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0036\n",
      "Epoch 117/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0037\n",
      "Epoch 118/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0037\n",
      "Epoch 119/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0038\n",
      "Epoch 120/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0037\n",
      "Epoch 121/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0037\n",
      "Epoch 122/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0038\n",
      "Epoch 123/1000\n",
      "143/143 [==============================] - 1s 8ms/sample - loss: 0.0035\n",
      "Epoch 124/1000\n",
      "143/143 [==============================] - 1s 7ms/sample - loss: 0.0037\n",
      "Epoch 125/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0042\n",
      "Epoch 126/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0041\n",
      "Epoch 127/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0036\n",
      "Epoch 128/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0037\n",
      "Epoch 129/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0037\n",
      "Epoch 130/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0035\n",
      "Epoch 131/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0036\n",
      "Epoch 132/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0035\n",
      "Epoch 133/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0039\n",
      "Epoch 134/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0037\n",
      "Epoch 135/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0036\n",
      "Epoch 136/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0035\n",
      "Epoch 137/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0037\n",
      "Epoch 138/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0035\n",
      "Epoch 139/1000\n",
      "143/143 [==============================] - 1s 8ms/sample - loss: 0.0034\n",
      "Epoch 140/1000\n",
      "143/143 [==============================] - 1s 7ms/sample - loss: 0.0035\n",
      "Epoch 141/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0037\n",
      "Epoch 142/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0032\n",
      "Epoch 143/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0034\n",
      "Epoch 144/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0034\n",
      "Epoch 145/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0033\n",
      "Epoch 146/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0033\n",
      "Epoch 147/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0032\n",
      "Epoch 148/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0038\n",
      "Epoch 149/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0031\n",
      "Epoch 150/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0032\n",
      "Epoch 151/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0031\n",
      "Epoch 152/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0033\n",
      "Epoch 153/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0032\n",
      "Epoch 154/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0031\n",
      "Epoch 155/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0031\n",
      "Epoch 156/1000\n",
      "143/143 [==============================] - 1s 7ms/sample - loss: 0.0030\n",
      "Epoch 157/1000\n",
      "143/143 [==============================] - 1s 8ms/sample - loss: 0.0030\n",
      "Epoch 158/1000\n",
      "143/143 [==============================] - 1s 7ms/sample - loss: 0.0031\n",
      "Epoch 159/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0034\n",
      "Epoch 160/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0032\n",
      "Epoch 161/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0032\n",
      "Epoch 162/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0031\n",
      "Epoch 163/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0031\n",
      "Epoch 164/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0031\n",
      "Epoch 165/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0039\n",
      "Epoch 166/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0031\n",
      "Epoch 167/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0029\n",
      "Epoch 168/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0030\n",
      "Epoch 169/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0030\n",
      "Epoch 170/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0030\n",
      "Epoch 171/1000\n",
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      "Epoch 949/1000\n",
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      "Epoch 950/1000\n",
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      "Epoch 951/1000\n",
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      "Epoch 952/1000\n",
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      "Epoch 953/1000\n",
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      "Epoch 954/1000\n",
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      "Epoch 955/1000\n",
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      "Epoch 956/1000\n",
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      "Epoch 957/1000\n",
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      "Epoch 958/1000\n",
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      "Epoch 959/1000\n",
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      "Epoch 960/1000\n",
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      "Epoch 961/1000\n",
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      "Epoch 962/1000\n",
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      "Epoch 963/1000\n",
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      "Epoch 964/1000\n",
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      "Epoch 965/1000\n",
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      "Epoch 966/1000\n",
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      "Epoch 967/1000\n",
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      "Epoch 968/1000\n",
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      "Epoch 969/1000\n",
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      "Epoch 970/1000\n",
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      "Epoch 971/1000\n",
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      "Epoch 972/1000\n",
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      "Epoch 973/1000\n",
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      "Epoch 974/1000\n",
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      "Epoch 975/1000\n",
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      "Epoch 976/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0012\n",
      "Epoch 977/1000\n",
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      "Epoch 978/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0011\n",
      "Epoch 979/1000\n",
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      "Epoch 980/1000\n",
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      "Epoch 981/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0012\n",
      "Epoch 982/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0012\n",
      "Epoch 983/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0012\n",
      "Epoch 984/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0012\n",
      "Epoch 985/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0012\n",
      "Epoch 986/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0013\n",
      "Epoch 987/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0012\n",
      "Epoch 988/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0012\n",
      "Epoch 989/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0011\n",
      "Epoch 990/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0011\n",
      "Epoch 991/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0011\n",
      "Epoch 992/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0011\n",
      "Epoch 993/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0011\n",
      "Epoch 994/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0012\n",
      "Epoch 995/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0012\n",
      "Epoch 996/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0012\n",
      "Epoch 997/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0013\n",
      "Epoch 998/1000\n",
      "143/143 [==============================] - 1s 5ms/sample - loss: 0.0012\n",
      "Epoch 999/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0012\n",
      "Epoch 1000/1000\n",
      "143/143 [==============================] - 1s 6ms/sample - loss: 0.0011\n"
     ]
    }
   ],
   "source": [
    "h = model.fit(x,y, batch_size=BATCHSIZE,\n",
    "              epochs = 1000)\n",
    "\n",
    "model.save('gdrive/My Drive/dataML/rnn_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 833128,
     "status": "ok",
     "timestamp": 1555022089429,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "bMwy2IIozaRt",
    "outputId": "4b971525-3745-4b88-c7a4-2341ca6042d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdde02fbe10>]"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QnO5raSwwXK5wZCAZPyzWJWEXEQRQrsRr+3B0qEypEsxuST68HBks0TtQor5Q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\n5UAwMzPAgWBmZikHgpmZAfD/Ac6PSqbJxSJRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(h.history['loss'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "r714-jBM1Yo-"
   },
   "source": [
    "### Predict Future \n",
    "* Use the last 12 monthes data of the train data set to predict the future 12 monthes production \n",
    "* Compare the predicted data with the real data in test data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BnGC-2ohfLwz"
   },
   "outputs": [],
   "source": [
    "model = models.load_model('gdrive/My Drive/dataML/rnn_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QkYcxMMgqGN0"
   },
   "source": [
    "Get the last 12 monthes data of the train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wN_zImU5_T-7"
   },
   "outputs": [],
   "source": [
    "train_seed = list(train_scaled[-12:].flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lJXmc5C_qPi4"
   },
   "source": [
    "One prediction only generate one predict, we need predict 12 times to get 12 monthes data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gPLrd2196eHx"
   },
   "outputs": [],
   "source": [
    "def get_prediction(data_list):\n",
    "  predict = []\n",
    "  train_seed = data_list\n",
    "  for i in range(12):\n",
    "    x_train = np.array(train_seed[-12:]).reshape(1,12)\n",
    "    one_predict = model.predict(x_train)[0][0]\n",
    "    predict.append(one_predict) \n",
    "    train_seed.append(one_predict)\n",
    "   \n",
    "  return predict, train_seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BT-RPo6k9XrQ"
   },
   "outputs": [],
   "source": [
    "predict, train_seed = get_prediction(train_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 752,
     "status": "ok",
     "timestamp": 1555022548018,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "f_p2HvS2Ce12",
    "outputId": "8556eba2-1b36-4e75-ce8e-be8ca55f893f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fddcb48a940>]"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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77iHQpjejKDsFK9R8YDGfFepUbFyZjZdOcd89WMEcuW8GoBdCdAshHACeB3DP\nPPe/H8CvQ1FcpPAyyMVrLNege2iC520vg8cjcKib++3B2r2uEGf7RqEfHJO6lJgQTLgXAegJ+L7X\nd9t1iKgEQBmAPy6/tMgx+ndgyuJwD5Z/zgwfvS/dmb5RWG1ONPMSyKDsrNdBRsAePnoPSqhPqN4H\n4EUhhHu2HxLRQ0R0jIiODQ0Nhfipl85g8Z6k4Z578Kp1mcjgvvuy+Ne3b+GTqUHJy0hGY7kGe08Z\nIYSQupyoF0y4GwCsCPi+2HfbbO7DPC0ZIcRTQogGIURDbm5u8FWGmcFih4yAAp4rEzSFXIbNZWqe\nM7MMbV1mVOSmIT+T33fB2r2uEN2mCXQaR6UuJeoFE+5HAVQRURkRqeAN8D0z70REawDkADgU2hLD\nz2ixIT8zGUo5rwxdjC0VGlwyTaDfyn33xXK6PXj30jCvklmklpoCKGTEq2aCsGCaCSFcAB4GcADA\nWQAvCCE6iegxIro74K73AXhexODnJV4GuTT+OTPcmlm8070WTDrcvL59kXLSVNi2Khd7T/XB44m5\nqImooA5VhRD7hBCrhBAVQojHfbc9KoTYE3CfbwkhrlsDHwsMHO5Lslbn3eeS58wsXpveDKJrf0Gy\n4O1ep4PBYsPJnhGpS4lqCd+H8HgE+ix2FPIa90WTywibyzS8YmYJWrtMqNZlIidNJXUpMef2tflI\nUsh4zfsCEj7cTRNTcLg9KOYj9yXZUqHB1eFJGHzXCrCF2Z1unLhi4ZbMEmUkK3HrmjzsPd0HN7dm\n5pTw4T69xp3DfUm2+Pvu3JoJ2vErI3C4PXwydRl2ryuEaXwKR/hT45wSPtwNI7wD03KsKchAdqqS\nT6ouQluXCXIZYVOZWupSYtata/KQppJjD0+KnFPChztvr7c8MhnhxjKe774YbV1mrCv2Dl9jS5Os\nlOPOmgK80tEPh8sjdTlRKeHD3WCxISNJwZt0LENjuQa9IzYexxqEMbsTp3ut3JIJgd3rdLDanDio\nj56r3aNJwoc7r3FfPv/l89yaWdi7l4bh9gg0VfLJ1OXaWpmLrBQlr5qZQ8KHu3eNOy+DXI5VeRlQ\np6m4NROEti4zVAoZNq7MkbqUmKdSyLCjrgCvdvbD5ph1nFVCS/hw5yP35fP33Y90D/NApwW0dZnR\nUJKDZKVc6lLiwu76Qkw43Hjz/KDUpUSdhA73SYcLI5NOnuMeAlsqNDBYbOgZ5vXucxmecOBs3yiv\nbw+hG8s10KYn8f6qs4i5cO+z2rCvPTQ9tulNOvjIfdl4zszC/K/NFj6ZGjJyGWFXvQ5/PDeIMbtT\n6nKiSsyF++9OGvD5X56AaXwgcwN7AAAT90lEQVRq2Y9l4AuYQqYqLx0a7rvPq63LhDSVHPXFWVKX\nEld2ryvElMuD188OSF1KVIm5cG/2HfW0heCKSD5yDx0iQmO5Boe6zNx3n0Nblxk3lmt4tHSIbVyZ\njaLsFOx5j1szgWLuXVZblIXMZAXa9KZlP5bRYoNcRsjLSApBZayxQoP+UTuumHm9+0z9Vju6hya4\n3x4GRIRd63T400UTRiYcUpcTNWIu3OUy7xHiwRCEu2HEhoLMZCj4SCok/HNmuDVzPd5SL7x21xfC\n5RHY39kvdSlRIyZTrblSi94RG64u8wiR17iHVkVuGnIzkvik6izauszITlVibUGm1KXEpZrCTJRr\n03jVTICYDXfAOxN7OYxWG/fbQ4j77rMTQuBQlxlbyjWQyUjqcuISEWH3ukIc6jZjcJS3fQRiNNy9\nmwonoXUZrRm3R6DfaueVMiHWWK7G4NgUuk0TUpcSNfzz7rnfHl671+kgBPByiJZKx7qYDHciQnOF\nFoe6zEveR3FobApOt+BwD7EtvN79Ov6VXU2VvL49nCrzMrBWl8mtGZ+YDHfA+wfFPOHA+YGxJf2+\ngZdBhkWZ1vupivdVvaZVb0J+ZhLKtWlSlxL3dq/T4cRVC08oRQyHe7Nvqt5SWzPTa9x59EBI+fvu\nh3nODIBr/famCi2IuN8ebrvrCwEAe09zayZmw12XlYJybdqyw12XxatlQm1LuQam8Sl0DY1LXYrk\nLgyMwzzh4CWQEbJCnYrNpWo8c7AbQ2PLv4o9lsVsuAPeVTPvXhqG0734nVgMFhsykxXISOZNOkKt\ncXq9+7DElUjH7nTjV0eu4rO/OAYZgU+mRtDjH6rFmN2Fv/5/p5Z8Ti4exHi4azDhcONUj2XRv8uj\nfsOnRJMKXVYyWi+aEq41M2p34j/e0uMD//omvv67dmQkK/H0gw0ozkmVurSEUZWfgX/YVY23Lwzh\n2dZLUpcjmZjexLGxXAMioFVvRkPp4jYbNljsKOZ+e1gQEZortXjxeC82Pf4GbijJRkOJGhtLclBb\nlIkkRfzNMh8YtePZg5fwyyNXMT7lwgeqtPjRR9djS4WGe+0S+NiNK/H2hSF8Z/85NJZrUFuUeMPa\nYjrcs1NVqC3MQqvehC/fXrWo3zWMTGJTKe+GEy7f2LEW61dk48SVERy7MoIDnd6JfSqFDPVFWbih\nNAc3rMzBDSU50KRHfraPddIJl8ez7OfuGhrHU29343cnDXB5PNhZX4jPbitPyDCJJkSE73ykHtt/\n9A6+/PxJvPTFrUhVxXTcLVrM/9c2VWrw7MFLmHS4gv6fN2Z3YtTu4rZMGOWkqfBAYwkeaCwBAAyO\n2XHiygiO+8L+2YOX8FN3NwDv8skbSrxB31yhxUpNeFsYbo/Ajh//CQaLDeo0FSrz0lHl+6cyLwNV\n+enIy0ia94j75NURPPl2F149MwCVXIZ7NxXjMx8oR4mGlztGC3WaCt+/dz0eeOYI/mnvWXz7w3VS\nlxRRMR/uzRVa/PTtbrx7aRg3r84L6nf6rDzHPdLyMpLRUqtDS60OgPeEY7vBimOXvYH/xtkBvHi8\nF0kKGfb/5TaUhXFN+PErIzBYbLi3oRhyGeHiwDj2nu6D1XZts4eMZIUv8L1hX+ELf/3gOJ58uwuH\nu4eRlaLEw7dU4hNNpdBK8OmDLay5UovPbqvAk2934aZV2un3XyKI+XDfVKqGSi5DW5c56HDnC5ik\nl6yUY1OpGpt850qEEOgwjOLuJw7ipVNGfOm2xbXZFmNfex9UChke3V2D9CTF9PMPjU9BPzAO/dA4\nLg6M4+LgGN44N4DfHOt53+8XZCbj73euxX2bV07/PoteX71jFdq6TPjb37ajvjg7YQ7qYv6dmaKS\nY2NJ9qLWuxtGONyjDRGhrjgLm0rUePl0X9jC3eMR2N/Rj5tW5b4vmIkIeRnJyMtIvm5MwPCEA/rB\ncVwYGENGsgLba3VQKWJ6oVlCUSlk+NF9G7Dzx3/CV37zHn71mUbIE2CAW1y8Q5srtDjTN4rhIAf1\nGy02KGSEXN6kI+rsWqfD+YExXFziWImFnOyxoH/Ujh11BUH/jjpNhc1lajzQWIJ71hdxsMegMm0a\n/vHuGhy5NIwn3+6SupyIiIt3aVOlFkIg6HkmRosNuuzkhPjbO9a01BaAKHyXj7/S3gelnHDb2vyw\nPD6LXn92QzF2ryvE91+7gJNXR6QuJ+ziItzXFWchPUkR9Hx3o8WOwixuyUSjvIxk3FimxsvtfSG/\nAEoIgVc6+vGBqlxk8pXJCYeI8M8frEVBZjK+/Px7GLM7F/6lGBZUuBNRCxGdJyI9ET0yx33uJaIz\nRNRJRL8KbZnzU8hluLFMHfS+qgYLb9IRzXbWF/p63KGdTXO61wqDxYbttcG3ZFh8yUpR4kf3rUfv\nyCS++YdOqcsJqwXDnYjkAJ4AsB1ANYD7iah6xn2qAPwdgGYhRA2AvwxDrfNqrtTisnlyeiXMXFxu\nD/pHeZOOaNZSUwAZAS+fDu1c7n0dfVDICHdUc0smkTWUqvGl26rwPycN+P1Jg9TlhE0wR+6bAeiF\nEN1CCAeA5wHcM+M+nwHwhBBiBACEEIOhLXNh01vvLXD0Pjg2BbdH8KjfKJabkYTGcg32hrA1I4TA\nK+39aKrUIjtVFZLHZLHr4Vsq0VCSg7//fcey92JeDLdH4G9ePIXTvYufh7VYwYR7EYDAhb69vtsC\nrQKwiohaiegwEbWEqsBgrcpPhzZ94a33/KN++cg9uu2s16F7aALn+kOzaqbTOIqrw5PYwS0ZBm8r\n94f3rQcR8KXnTy5psuxS/PZEL1441ourEdhMJFQnVBUAqgDcDOB+AE8TUfbMOxHRQ0R0jIiODQ0N\nheippx8bTRUatC2wOfO1C5h4jns0a6kpgFxGeDlEq2b2d/RDLiPcWcPhzryKc1Lx+Ifq8F6PBT9+\n42LYn8/mcOP7r17AuhXZ2FkX/itlgwl3A4AVAd8X+24L1AtgjxDCKYS4BOACvGH/PkKIp4QQDUKI\nhtzc3KXWPKfmSg2GxqZwcXDuE3EGPnKPCZr0JDRVaLD3tHHZrRkhBPa196GxXA11Grdk2DV3ryvE\nn91QjP/7pj7s+/4+23oJ/aN2fH37mohMCg0m3I8CqCKiMiJSAbgPwJ4Z9/k9vEftICItvG2a7hDW\nGZRg+u5Giw05qcqEmxAXi3bW6XDZPIlO4+iyHufCwDi6TRPYnkBzRVjwvnV3DUrUqfjqb8K3PNI8\nPoWfvNWF29fm48byyGzcsmC4CyFcAB4GcADAWQAvCCE6iegxIrrbd7cDAMxEdAbAmwC+JoSI+A7J\nxTmpKNGkolU/91MbLbxSJlbc5W/NtC+vNbOvvQ9E3sdjbKb0JAW+/9H16Bu147sHzoflOf79j3pM\nOlx4ZPvqsDz+bILquQsh9gkhVgkhKoQQj/tue1QIscf3tRBCfFUIUS2EqBNCPB/OoufTVKHFkW4z\nXHOcIDGM8A5MsSInTYXmSi1ePr28VTOvdPRhc6max02wOW1cmYNPbCnFLw5fwbHLod0e8rJpAv99\n+Ao+umklKvMyQvrY84mLK1QDNVdqMDblwmmDddafG/kCppiyq06Hq8OT6DAsrTWjHxzDhYFx7IjA\nCSwW275212oUZqXgb397GlMud8ge97sHzkOlkOErd4Rv0uls4i7ct/j6WbNdrTpqd2JsysXhHkPu\nrMmHQkbY2760C5peae8H4J1Zw9h80pIU+OcP1aJraAJPvBma4WInro7g5fY+fOYD5cjLiOwKvbgL\nd016EtbqMmftu/tH/XJbJnZkp6qwtWrprZl9Hf1oKMlBfiYvfWULu2V1Hj64vhA/eUuP88u8xkII\ngW/vOwttehIe2lYeogqDF3fhDgBbKzU4fnUEduf7P1pdu4CJ/6DHkp11OvSO2HCqd/ZW21wumSZw\ntm8U27klwxbh0d01yEhW4m9/expuz9LP9bx2ZgBHL4/gK3dUIU2CTV3iMtybKrVwuDw4dvn9Yz2N\nvANTTLqzpgBKOS161swrHd5VNtySYYuhTlPh0V3VeK/HgufaLi/pMZxuD/5l/zmU56bhow0rFv6F\nMIjLcN9cqoZCRteNADZY7FDJZbzfZYzJSlFiW1Xuolszr7T3Y/2KbP7LnC3aPesLcfPqXHzv1fPo\nWcKogN8c7UH30AQeaVkDhVyamI3LcE9LUmDDyuu33jP4NumQ8SYdMWdnvQ5Gqx0ne4IbuNQzPIl2\ng3VROy4x5uef/Q4A3/h9x6IOKsanXPjh6xewqTRH0gmkcRnugHe9e7vBCuvktSvOjBYbb9IRo26v\nzodKLgt61oy/JcNXpbKlKs5Jxd/ctRrvXBjC798LfjTwU+90wzTuwNd3rI3ImIG5xG24N/u33guY\nF2G02HjUb4zKTFZi26pc7GvvgyeIk1z72vtRV5SFFerUCFTH4tXHt5Riw8psPPbSGZjHpxa8/+Co\nHU+/042ddTpsWJkTgQrnFrfhvn5FNlJVcrT5+u5OtwcDvElHTNtVr0Of1Y6TPfPvf2mw2PBejwXb\nuSXDlkkuI3znI/UYn3Lhsb1nFrz/D16/CJfHg6/dFbkxA3OJ23BXKWTYXKae7rv3W+3wCB71G8tu\nW5sHlUKGl07N35rZ3+G9cIlbMiwUVuVn4PM3V+IP7xnx5rm59yHSD47hN0ev4mM3lqBUmxbBCmcX\nt+EOAM0VWnQNTaDfaudNOuJARrISNwfRmnmlvQ9rdZkoi4I/YCw+fP6WClTlpeMbv2vH+JRr1vv8\nyyvnkKZS4Iu3Vka4utnFdbg3VXpHEbTqTTBaeY17PNhZr8Pg2BSOXZm9NdNvtePYlRHecYmFVJJC\njn/5SD36Ru343iyTIw93m/H62UF87uYKaKJkqXVch/vagkyo01Ro7TLBaLED4CP3WHf72nwkKWRz\nXtB0oNPXkuGrUlmI3VDinRz53KHLOB5wcOEfM6DLSsant5ZJV+AMcR3uMhlhS7kGbXozekds0KSp\nkKyUS10WW4a0JAVuXZOHfR39s14avq+9D6vy01GZly5BdSze/fVdq6HLTMYjAZMj957uw6leK756\nx6qoype4DnfAuySyf9SOVr2Jj9rjxM56HYbGpnB0xtztobEpvHt5mE+ksrBJT1Lg8Q/X4eLgOH7y\nVhemXG7864FzWFOQgQ9vLJa6vPdJgHD39t2vDk9yvz1O3LomD8nK6y9oOtDZDyHAs9tZWN2yOg/3\nrC/EE2/q8dhLZ9AzbMMj29dAHmVXvsd9uK9Up06HOh+5x4dUlQK3rcnHKx1979txa39HP8pz07Aq\nn1syLLwe3VWN9CQFfnnkKrZWanHTqlypS7pO3Ic7EU0fvfOo3/ixs14H07gD717ytmaGJxw41G3G\njlqdpJd8s8SgSU/CP32wFuo0Ff5ux5qofM/FfbgD3r47wMsg48ktq/OQopRjr2/z7NfOeE+w8lWp\nLFJ21Rfi6DduR01hltSlzCohwv3O6gJ86dZKbIvCj05saVJUcty2Ng/7O/rhcnuwr70fJZpUVOsy\npS6NJZBo67MHSohwT1HJ8dU7V0uyGwoLn131hRiecOBA5wBa9SZs55YMY9MSItxZfLp5dS7SVHJ8\nc08nXB7Bs9sZC8DhzmJWslKO26vzYRqfQlF2CuqKorP3yZgUONxZTNvpW9O+o66AWzKMBeAmNItp\nN6/Ow0PbyvGJplKpS2EsqnC4s5imUsjw9R1rpS6DsajDbRnGGItDHO6MMRaHONwZYywOcbgzxlgc\n4nBnjLE4xOHOGGNxiMOdMcbiEIc7Y4zFIRLi+k2GI/LEREMArizx17UATCEsJ1bx63ANvxZe/Dp4\nxfPrUCKEWHB+uWThvhxEdEwI0SB1HVLj1+Eafi28+HXw4teB2zKMMRaXONwZYywOxWq4PyV1AVGC\nX4dr+LXw4tfBK+Ffh5jsuTPGGJtfrB65M8YYm0fMhTsRtRDReSLSE9EjUtcjFSK6TETtRPQeER2T\nup5IIaJniWiQiDoCblMT0WtEdNH37xwpa4yUOV6LbxGRwfe+eI+IdkhZY7gR0QoiepOIzhBRJxF9\n2Xd7Qr4nAsVUuBORHMATALYDqAZwPxFVS1uVpG4RQqxPsCVfPwfQMuO2RwC8IYSoAvCG7/tE8HNc\n/1oAwA9874v1Qoh9Ea4p0lwA/koIUQ2gEcAXfJmQqO+JaTEV7gA2A9ALIbqFEA4AzwO4R+KaWAQJ\nId4BMDzj5nsAPOf7+jkAH4xoURKZ47VIKEKIPiHECd/XYwDOAihCgr4nAsVauBcB6An4vtd3WyIS\nAF4louNE9JDUxUgsXwjR5/u6H0C+lMVEgYeJ6LSvbZMw7QgiKgWwAcAR8Hsi5sKdXbNVCLER3hbV\nF4hom9QFRQPhXf6VyEvAfgKgAsB6AH0A/k3aciKDiNIB/BbAXwohRgN/lqjviVgLdwOAFQHfF/tu\nSzhCCIPv34MAfgdvyypRDRCRDgB8/x6UuB7JCCEGhBBuIYQHwNNIgPcFESnhDfZfCiH+x3dzwr8n\nYi3cjwKoIqIyIlIBuA/AHolrijgiSiOiDP/XAO4E0DH/b8W1PQA+4fv6EwD+IGEtkvIHms+HEOfv\nCyIiAM8AOCuE+H7AjxL+PRFzFzH5lnb9EIAcwLNCiMclLiniiKgc3qN1AFAA+FWivA5E9GsAN8M7\n9W8AwDcB/B7ACwBWwjtp9F4hRNyfaJzjtbgZ3paMAHAZwGcDes9xh4i2AvgTgHYAHt/NX4e3755w\n74lAMRfujDHGFhZrbRnGGGNB4HBnjLE4xOHOGGNxiMOdMcbiEIc7Y4zFIQ53xhiLQxzujDEWhzjc\nGWMsDv1/c27gFmZpPAYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "H5VAR8RQ98LN"
   },
   "outputs": [],
   "source": [
    "results = scaler.inverse_transform(np.array(predict).reshape(12,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 873,
     "status": "ok",
     "timestamp": 1555022549636,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "bJgzXnlWDMp3",
    "outputId": "6d6ffed5-0e92-47b4-bd35-710fcd0dee35"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "test_set['Generated'] = results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "omtVz1Crqo9f"
   },
   "source": [
    "Compare the generated data with the real data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 436,
     "status": "ok",
     "timestamp": 1555022550715,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "bPZAXKyMDVAD",
    "outputId": "18a1a768-b04c-4a3f-9931-a459eceb33f3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Milk Production</th>\n",
       "      <th>Generated</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1975-01-01 01:00:00</th>\n",
       "      <td>834.0</td>\n",
       "      <td>810.854858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-02-01 01:00:00</th>\n",
       "      <td>782.0</td>\n",
       "      <td>816.955444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-03-01 01:00:00</th>\n",
       "      <td>892.0</td>\n",
       "      <td>867.727295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-04-01 01:00:00</th>\n",
       "      <td>903.0</td>\n",
       "      <td>917.369995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-05-01 01:00:00</th>\n",
       "      <td>966.0</td>\n",
       "      <td>967.728638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-06-01 01:00:00</th>\n",
       "      <td>937.0</td>\n",
       "      <td>954.208435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-07-01 01:00:00</th>\n",
       "      <td>896.0</td>\n",
       "      <td>906.976013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-08-01 01:00:00</th>\n",
       "      <td>858.0</td>\n",
       "      <td>856.281189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-09-01 01:00:00</th>\n",
       "      <td>817.0</td>\n",
       "      <td>818.201355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-10-01 01:00:00</th>\n",
       "      <td>827.0</td>\n",
       "      <td>799.957764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-11-01 01:00:00</th>\n",
       "      <td>797.0</td>\n",
       "      <td>779.535156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-12-01 01:00:00</th>\n",
       "      <td>843.0</td>\n",
       "      <td>803.476807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Milk Production   Generated\n",
       "Month                                           \n",
       "1975-01-01 01:00:00            834.0  810.854858\n",
       "1975-02-01 01:00:00            782.0  816.955444\n",
       "1975-03-01 01:00:00            892.0  867.727295\n",
       "1975-04-01 01:00:00            903.0  917.369995\n",
       "1975-05-01 01:00:00            966.0  967.728638\n",
       "1975-06-01 01:00:00            937.0  954.208435\n",
       "1975-07-01 01:00:00            896.0  906.976013\n",
       "1975-08-01 01:00:00            858.0  856.281189\n",
       "1975-09-01 01:00:00            817.0  818.201355\n",
       "1975-10-01 01:00:00            827.0  799.957764\n",
       "1975-11-01 01:00:00            797.0  779.535156\n",
       "1975-12-01 01:00:00            843.0  803.476807"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 901,
     "status": "ok",
     "timestamp": 1555022552978,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "6v-Ot8ZxDZvi",
    "outputId": "656d72cf-d691-48ef-c6cb-2328ce54b10b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdde02b1b00>"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
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12xN/LZ0fdkTR86sd9PhqBz/tisGvWnnmPR3AE/73Tuko91fT3ZF3ezdk56kr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\nx4kcd/SO4ziR8/+TZkoRSmtitAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_set.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WkrgRIqzkXgq"
   },
   "source": [
    "### Predict more \n",
    "Use the first months data to predict the future 13 years' milk productions and compare with the real data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sbRNA4ERklYA"
   },
   "outputs": [],
   "source": [
    "train_seed = list(train_scaled[:12].flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F91e0IzvklYE"
   },
   "outputs": [],
   "source": [
    "def get_prediction(data_list):\n",
    "  predict = []\n",
    "  train_seed = data_list\n",
    "  for i in range(12*13):\n",
    "    x_train = np.array(train_seed[-12:]).reshape(1,12)\n",
    "    one_predict = model.predict(x_train)[0][0]\n",
    "    predict.append(one_predict) \n",
    "    train_seed.append(one_predict)\n",
    "   \n",
    "  return predict, train_seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BNW_8HJ2klYG"
   },
   "outputs": [],
   "source": [
    "predict, train_seed = get_prediction(train_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3615,
     "status": "ok",
     "timestamp": 1555023270581,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "TvgurkwHklYI",
    "outputId": "fefd099b-b47a-4ad7-93cd-522994912571"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fddcafe0588>]"
      ]
     },
     "execution_count": 86,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lah35NiZngCifvAERvsWIWQh+qzWygDGGhAUbhHSh2raaqBmld2BrWyST4BNEF1nNKVOk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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pcNqRCbiklYN"
   },
   "outputs": [],
   "source": [
    "results = scaler.inverse_transform(np.array(train_seed).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K-8x-sOFmBav"
   },
   "outputs": [],
   "source": [
    "milk_predict = milk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "387zn4SBmKsl"
   },
   "outputs": [],
   "source": [
    "milk_predict['Generated'] = results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 664,
     "status": "ok",
     "timestamp": 1555023317996,
     "user": {
      "displayName": "Hongtao Zhang",
      "photoUrl": "https://lh5.googleusercontent.com/-XK0YfSQSDhA/AAAAAAAAAAI/AAAAAAAAIoM/eIY5kmxK00c/s64/photo.jpg",
      "userId": "14551110502303433991"
     },
     "user_tz": -60
    },
    "id": "PDvQXKtjklYX",
    "outputId": "b6bc252d-d54c-47fd-b875-da4a9e4cc2da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fddcafea240>"
      ]
     },
     "execution_count": 92,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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es0J+u/K3jKZ3sc0bI1WotBRTABDPlehS4lCbSePtkVW3qho56RPx5ojIU02R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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "milk_predict.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zIAwIfd6JDa8"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "1_RNN_Many_to_One_Keras.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
